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57 results found

Per- and polyfluoroalkyl substances (PFAS) Interferents List

Data provided by  National Institute of Standards and Technology

A table of known interferences for per- and polyfluoroalkyl substances using mass spectrometry.

Tags: per- and polyfluoroalkyl substances,PFAS,mass spectrometry,analytical chemistry,high-resolution mass spectrometry,HRMS,LC-MS/MS,

Modified: 2024-02-22

Views: 0

Database Infrastructure for Mass Spectrometry - Per- and Polyfluoroalkyl Substances

Data provided by  National Institute of Standards and Technology

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis.

Tags: per- and polyfluoroalkyl substances,PFAS,database,mass spectrometry,SQLite,emerging contaminants,high resolution mass spectrometry,non-targeted analysis,NTA,HRMS,

Modified: 2024-02-22

Views: 0

Noise Datasets for Evaluating Deep Generative Models

Data provided by  National Institute of Standards and Technology

Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions.

Tags: generative adversarial network,machine learning,time series,band-limited noise,power law noise,shot noise,impulsive noise,colored noise,fractional Gaussian noise,fractional Brownian motion,

Modified: 2024-02-22

Views: 0

Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning

Data provided by  National Institute of Standards and Technology

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

Tags: Microplastic,Nanoplastics,environment,mass spectrometry,GC-MS,Chemical Characterization,machine learning,

Modified: 2024-02-22

Views: 0

ns-3 ORAN Module

Data provided by  National Institute of Standards and Technology

This module for ns-3 implements the classes required to model a network architecture based on the O-RAN Alliance's specifications. These models include a Radio Access Network (RAN) Intelligent Controller (RIC) that is functionally equivalent to O-RAN's Near-Real Time (Near-RT) RIC, and reporting modules that attach to simulation nodes and serve as communication endpoints with the RIC in a similar fashion as the E2 Terminators in O-RAN.

Tags: Artificial Intelligence,machine learning,Open RAN,RAN Intelligent Controller,

Modified: 2024-02-22

Views: 0

Towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) MIg analyZeR (mizr) Package

Data provided by  National Institute of Standards and Technology

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots.

Tags: analysis software,Artificial Intelligence,machine learning,design of experiments,

Modified: 2024-02-22

Views: 0

A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks

Data provided by  National Institute of Standards and Technology

Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide.

Tags: 3D printing,additive manufacturing,machine learning,generative adversarial network,Photopolymer,

Modified: 2024-02-22

Views: 0

FCpy: Feldman-Cousins Confidence Interval Calculator

Data provided by  National Institute of Standards and Technology

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,

Modified: 2024-02-22

Views: 0

Segmentation of lipid nanoparticles from cryogenic electron microscopy images

Data provided by  National Institute of Standards and Technology

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Tags: Lipid Nanoparticle,LNP,cryogenic electron microscopy,CryoEM,machine learning,ai,mRNA,

Modified: 2024-02-22

Views: 0

Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models

Data provided by  National Institute of Standards and Technology

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

Tags: time series,machine learning,band-limited noise,power law noise,shot noise,impulsive noise,colored noise,fractional Gaussian noise,fractional Brownian motion,

Modified: 2024-02-22

Views: 0